BioMaPS Institute, Rutgers University, Piscataway, NJ, USA.
Skin Res Technol. 2012 Aug;18(3):290-300. doi: 10.1111/j.1600-0846.2011.00569.x. Epub 2011 Sep 6.
Computer-aided diagnosis of dermoscopy images has shown great promise in developing a quantitative, objective way of classifying skin lesions. An important step in the classification process is lesion segmentation. Many studies have been successful in segmenting melanocytic skin lesions (MSLs), but few have focused on non-melanocytic skin lesions (NoMSLs), as the wide variety of lesions makes accurate segmentation difficult.
We developed an automatic segmentation program for detecting borders of skin lesions in dermoscopy images. The method consists of a pre-processing phase, general lesion segmentation phase, including illumination correction, and bright region segmentation phase.
We tested our method on a set of 107 NoMSLs and a set of 319 MSLs. Our method achieved precision/recall scores of 84.5% and 88.5% for NoMSLs, and 93.9% and 93.8% for MSLs, in comparison with manual extractions from four or five dermatologists.
The accuracy of our method was competitive or better than five recently published methods. Our new method is the first method for detecting borders of both non-melanocytic and melanocytic skin lesions.
计算机辅助诊断的皮肤镜图像已显示出巨大的希望，在开发一种定量、客观的方法来对皮肤病变进行分类。在分类过程中一个重要的步骤是病变分割。许多研究已经成功地分割了黑素细胞性皮肤病变（MSL），但很少有研究关注非黑素细胞性皮肤病变（NoMSL），因为病变的多样性使得准确分割变得困难。
我们开发了一种用于检测皮肤镜图像中皮肤病变边界的自动分割程序。该方法包括预处理阶段、一般病变分割阶段，包括光照校正和亮区分割阶段。
我们在一组 107 个非黑素细胞性病变和一组 319 个黑素细胞性病变上测试了我们的方法。与四位或五位皮肤科医生的手动提取相比，我们的方法在非黑素细胞性病变的精度/召回率为 84.5%和 88.5%，在黑素细胞性病变的精度/召回率为 93.9%和 93.8%。
我们的方法的准确性与最近发表的五种方法相当或更好。我们的新方法是第一个用于检测非黑素细胞性和黑素细胞性皮肤病变边界的方法。
